SVC & TCSC for Minmum Operational Cost Under Different Loading Condition
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1 SVC & TCSC for Minmum Operational Cost Under Different ing Condition Biplab Bhattacharyya 1 Associate Professor, Electrical Engg. Indian School of Mines, Dhanbad, India biplabrec@yahoo.com S.K.Goswami 2 Professor, Electrical Engg. dept. Jadavpur University Kolkata, India skgoswami_ju@yahoo.co.in Vikash Kumar Gupta 3 Research Scholar, Electrical Engg. Indian School of Mines, Dhanbad, India vikash1146@gmail.com Abstract This paper presents Genetic Algorithm (GA) based approach for the allocation & coordinated operation of multiple (Flexible AC Transmission System) devices for the improvement of power transfer capacity in an interconnected power system. Static VAr Compensator (SVC) and the Thyristor Controlled Switched Capacitor, these two devices are used for the improvement of the power transfer ability of the system in our proposed method. The GA based approach is applied on IEEE 30-bus system. The system is reactively loaded starting from base to 200 % of base load. devices are installed in the different locations of the power system and system performance is noticed with and devices. First, the locations, where the devices to be placed is determined by calculating active and reactive power flows in the lines. A Genetic Algorithm based algorithm is then applied to find the amount of magnitudes of the devices. This approach of Genetic Algorithm based placement of devices is tremendous beneficial both in terms of performance and economy which is clearly observed from the result obtained. Keywords- Devices, Line, Optimal Location of Devices, Genetic Algorithm F I. INTRODUCTION ACTS technology is being considered to be an effective means for greater utilization & better control of existing current carrying capacity of a power system. It is known that the power flow through an ac transmission line is a function of line impedance, the magnitude and the phase angle between the sending end and the receiving end voltages. By proper coordination of UPFC (Unified Controller), TCSC (Thyristor controlled Series Capacitor) & SVC (Static Var Compensator) in the power system network, both the active and reactive power flow in the lines can be controlled. Tighter control of power flow and the increased use of transmission capacity by devices are discussed in [1]. A scheme of power flow control in lines is discussed in [2]. Use of static phase shifters and controllers for the purpose of increasing power transfer capacity in the transmission line is described in [3] & [4]. In [5] authors have discussed about the power flow control in transmission network. About the modeling and selection of possible locations for the installation of devices have been discussed in [6]. Assessment and impact on power networks by the use of devices have been discussed in [7] through the concept of steady state security regions. Allocation of variable series capacitor & static phase shifters in transmission lines was the main objective in [8] for the optimal power flow. A hybrid Genetic Algorithmic approach with devices for optimal power flow is dealt in [9]. In a congested power system, first the locations of the devices were decided based on the sensitivity factors and then dispatch problem was solved in [10]. A GA based separate & simultaneous use of Thyristor Controlled Series Capacitor (TCSC), Unified Controller (UPFC), Thyristor Controlled Voltage regulator (TCVR), and Static Var Compensator (SVC) were studied in [11] for increased power flow. The objective of this present work is the optimal allocation of devices in the transmission network so the transmission loss becomes minimized and also for the simultaneous increase of power transfer capacity of the transmission network. Minimization of transmission loss is a problem of reactive power optimization and can be done by controlling reactive generations of the generators, controlling transformer tap positions and adding shunt capacitors in the weak buses [12] but the active power flow pattern can not be controlled. GA based optimization technique [14] is discussed for the placement of devices in some Test systems. In the proposed work, first the locations of the devices are identified by calculating different line flows. Voltage magnitude and the phase angle of the sending end buses of the lines where major active power flow takes place are controlled by UPFC. TCSC s are placed in lines where reactive power flows are very high and the SVC s are connected at the receiving end buses of the other lines carrying significant amount of reactive power. In this proposed work, a Genetic Algorithm based approach considering the simultaneous effect of the three types of the devises are presented and the effectiveness of this technique is clearly evident from the result shown. 1
2 II. DEVICES A. Modelling of Devices & cost functions Mathematical modeling of devices are required for the steady state analysis. Here the devices used in the transmission network are TCSC and SVC. TCSC By modifying the line reactance TCSC acts as either inductive or capacitive compensator. The maximum value of the capacitance is fixed at -0.8 X Line and 0.2X Line is the maximum value of the inductance. Transmission line admittance in which TCSC is connected can be written as G tcsc +jb tcsc = (1) 1 R j(x Line Xtcsc) where R and X Line are the resistance and reactance of the line TCSC. SVC The SVC can be operated as either inductive or capacitive compensation. It can be modeled as a fixed capacitor and a thyristor controlled reactor. So function of the SVC is either to inject reactive power to bus or to absorb reactive power from the bus where it is connected. The SVC's effective reactance X SVC is determined by parallel combination of X C & X L and is given by X SVC = XCXL X [2( - ) 2sin ]- X C where is the firing angle. In our approach, obtaining the firing angle for the determination of X SVC is not required as GA itself generates the value of X SVC either as capacitive or inductive element. B. Devices cost Functions According to [13], cost functions for SVC, and TCSC are given below : TCSC: C TCSC =0.0015(OR) (OR) (US$/kVar) (3) SVC: C SVC =0.0003(OR) (OR) (US $/kvar) (4) Here, (OR) is the operating range of the Devices. L (2) III. OPTIMAL PLACEMENT OF DEVICES Having made the decision to install a device in the system, there are three main issues that are to be considered: types of device, its capacity and location. The decision where they are to be placed is largely dependent on the desired effect and the characteristics of the specific system. SVC s are mostly suitable when reactive power flow or voltage support is necessary. Also the costs of the devices play an important role for the choice of a device. There are two distinct means of placing a device in the system for the purpose of increasing the system s ability to transmit power, thereby allowing for the use of more economic generating units. That is why devices are placed in the more heavily loaded lines to limit the power flow in that line. This causes more power to be sent through the remaining portions of the system while protecting the line with the device for being overloaded. This method which sites the devices in the heavily loaded line is the most effective. If reactive power flow is a significant portion of the total flow on the limiting transmission line, either a TCSC device in the line or a SVC device located at the end of the line that receives the reactive power, may be used to reduce the reactive power flow, thereby increasing the active C. Genetic Algorithm in the proposed method The function of the GA is to find the optimum value of the different devices. Here two different types of devices are used. And for each type of devices, four positions are assigned. Four TCSC modifies reactance of four lines. Similarly four SVC s are to control reactive injection at four buses. In addition transformer tap positions along with reactive generations of the Generators are controlled. In IEEE 30y bus system there are four tap positions and five Generator Buses. So, as a whole seventeen values are to be optimized by Genetic Algorithm. These seventeen controlling parameters are represented with in a string. This is shown in figure 1. Initially a population of N strings is randomly created in such a way so that the parameter values should be with in their limits. Then the objective function is computed for every individual of the population. A biased roulette wheel is created from the values obtained after computing the objective function for all the individuals of the current population. Thereafter the usual Genetic operation such as Reproduction, Cross-over & Mutation takes place. Two individual are randomly selected from the current population for reproduction. Then Cross-over takes place with a probability close to one (here 0.8). Finally mutation with a specific probability (very low) completes one Genetic cycle and individuals of same population with improved characters are created in the next generation. The objective function is then again calculated for all the individual of the new generation and all the genetic operations are again 2
3 performed and the second generation of same population size is produced. This procedure is repeated till the final goal is achieved. IV. TEST RESULTS The proposed technique for the placement of devices is applied on IEEE 30 Bus system. The power system is loaded (reactive loading is considered) and devices are placed at different locations of the power system. The power system is loaded upto the limit of 200% of base reactive load and accordingly the system performance is observed with and devices. Table 1 shows the locations of different devices in the transmission network. Table 2 shows the active & reactive power flow pattern devices in different lines. Table 3 shows the active & reactive power flow pattern with devices in different lines using GA based approach, where as, the magnitude and phase angle of the bus voltages with & devices for highest reactive loading i.e. for 200% is shown in Table 4. Phase angles are given in radian. A comparative study of the operating cost of the system with and devices using GA is given in Table 5. It is observed from Table 1, that SVC s are connected at the buses 21, 7, 17 & 15, the finishing ends of the lines 27, 26, 9, 18 respectively, since these are the four lines carrying highest, second highest, third & fourth highest reactive power respectively, as seen from Table 2, devices. After connecting SVC s at these buses, voltage profile at these buses are improved, also reactive power flow reduces in the lines 27, 26, 9, 18. There is a slight increase of reactive power flow in line 9, in case of base loading with devices. TCSC s are placed in the lines 5, 18, 25 & 41, as these are the next four highest reactive power carriers as seen from Table 2. It is also to be noticed that no device is connected in line 1 because of the fact that it is in between bus 1 and bus 2 though it carries very large active power. Bus 1 is the slack bus and already a device regulates the voltage of the bus 2. Again in any line or in a bus connected with the line, only one device can be placed. From Table 5, we observe that transmission loss is reduced significantly with devices as compared to such devices. A significant economic gain is obtained even at a loading of 200% of base reactive loading which is also evident from Table 5. Here, energy cost is taken as 0.06$/kWh. V. TABLES & FIGURES TABLE I LOCATIONS OF DIFFERENT DEVICES IN THE TRANSMISSION NETWORK TABLE2: ACTIVE & REACTIVE POWER FLOW WITHOUT DEVICES Base TCSC in Lines 150% Base SVC in Buses 25, 41, 28, 5 21, 7, 17, % Base % Base Figure 1. Shows the different devices to be installed in the system with in a string. Figures 2 to 5 shows the variation of operating cost with generation for different cases of reactive loading of the system with GA based Algorithm. 3
4 Base TABLE3: ACTIVE & REACTIVE POWER FLOW WITH DEVICES 150% Base 175% Base 200% Base Bus Voltage TABLE 4: BUS VOLTAGES & PHASE ANGLES WITH AND WITHOUT DEVICES FOR 200% REACTIVE Bus Voltage with LOADING Bus Angle Bus Angle TABLE 5: COMPARATIVE ANALYSIS OF ACTIVE POWER LOSS AND OPERATING COST USING GA ing loss Operating cost due to energy loss 10 6 loss with using GA Operating cost with devices 10 6 Cost of devices Net Saving 10 6 (A) (in $) (in $) (in $) (B) (A-B) 100% % % %
5 TCSC Elements (4 nos) Shunt Elements (4 nos) Transfer Tap (4 nos) Generations (5 nos) Figure 1. String Representing the Control Variables Figure 4. Variation of operating cost with Generation for reactive loading of 175% with GA Figure 2. Variation of operating cost with Generation for base reactive loading with GA Figure 5. Variation of operating cost with Generation for reactive loading of 200 % with GA VI. CONCLUSIONS Figure 3. Variation of operating cost with Generation for reactive loading of 150 % with GA In this approach, GA (Genetic Algorithm) based optimal placement of devices in a transmission network is done for the increased load ability of the power system as well as to minimize the transmission loss. Two different types of devices are considered. It is clearly evident from the results that effective placement of devices at 5
6 proper locations by using suitable optimization technique can significantly improve system performance. Hence, this GA based approach could be a new technique for the installation of devices in the transmission system. REFERENCES [1] N. Hingorani, Flexible AC Transmission, IEEE Spectrum, Vol. 30, No. 4, pp , April [2] M. Noroozian, G. Anderson, Control by use of controllable Series Components, IEEE Trans. Delivery, Vol. 8, No. 3, pp , July [3] M. Iravani, P. L. Dandeno, and D. Maratukulam, Application of Static Phase Shifters in Systems, IEEE Trans Delivery, Vol. 9, No. 3, pp , July [4] D. Ramey, R. Nelson, J. Bian, and T. Lemak, Use of Controllers to enhance Transmission Transfer Limits, Proceedings American Conference, Vol. 56, Part 1, pp , April [5] R. Nelson, J. Bian, and S. Williams, Transmission Series Control, IEEE Trans. Delivery, Vol. 10, No. 1, pp , Jan [6] D.J. Gotham and G.T.Heydt, Control and Studies for System with Devices, IEEE Trans. System, Vol. 13, No. 1, pp , Feb [7] F.D. Galiana, K. Almeida, Assessment and Control Of The Impact Of Devices On System Performance, IEEE Transactions on Systems, Vol. 11, No. 4, pp , Nov [8] T.T. Lie and W. Deng, Optimal Flexible AC Transmission Systems () devices allocation, Int. Journal of Electrical & Energy Systems, Vol. 19, No. 2, pp , [9] T.S. Chung and Y.Z. Li, A Hybrid GA approach for OPF with Consideration of Devices, IEEE Engineering Review, pp , Aug [10] S.N. Singh and A.K. David, Optimal location of devices for congestion management, Electric System Research Vol. 58, pp , [11] S.Gerbex, R. Cherkaoui, and A.J. Germond, Optimal Location of Multitype Devices in a System by Genetic Algorithm, IEEE Trans. Systems, Vol. 16, pp , Aug [12] B.Bhattacharyya, S.K.Goswami, R.C.Bansal, Loss- Sensitivity Approach in Evolutionary Algorithms for Planning Electric Components & Systems, Vol. 37, No. 3, 2009, pp [13] L.J. Cai, Optimal Choice and Allocation of Devices in Deregulated Electricity Market Using Genetic Algorithms IEEE, X/04/2, [14] D.E. Goldberg, Genetic Algorithms in Search, Optimization & Learning, Addison-Wesley, New York USA. 6
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